NYS IPM Sweet Corn Long-Term Pest Data

Introduction

NYS IPM sweet corn pheromone trapping dataset 1993-2022. Data was initially used as a monitoring tool to inform growers about corn pest populations.

LINKS/RESOURCES

• EDA Slide Deck
• Pest population dynamics are related to a continental overwintering gradient
• Pest Data Sheets

LIMITATIONS & NOTES

• GPS coordinates are not precise because traps moved around from year to year depending on where sweet corn was planted. Some farms are much larger than the typical 1000m radius that we use.
• The timing of trap placement varied from year to year, shifted earlier for certain pests when they began to emerge earlier.
• Blanks are NAs (no data collected since traps were not set up yet), zeros are actual observations of no pest at that sampling event.

PESTS

• European Corn Borer (ECB) (E and Z strains for sex pheromones)
• Corn Earworm (CEW)
• Western Bean Cutworm (WBC)
• Fall Armyworm (FAW)

Read Data

Read in csv file. Columns Site, USDA Hardiness Zone, Latitude, Longitude, Year, Week, Date, Day, ECB.E, ECB.Z, CEW, FAW, and WBC. Each row is a sampling event at a farm.

Melt data frame to create a row for each observation, creating a column for the pest name and a column for the pest count.

sweet_corn_pest <- read.delim("NYSIPM_Sweet_Corn_1993_2022.csv", sep = ",")

sweet_corn_pest <- sweet_corn_pest %>% 
  mutate(Date = as.Date(Date, format = '%m/%d/%y')) %>%
  mutate(Site = as.factor(Site)) %>%
  mutate(USDA.hardiness.Zone = as.factor(USDA.hardiness.Zone)) %>%
  mutate(new_col = format(Date, "%j"), .after = Date) %>%
  rename(Day = new_col)

sweet_corn_pest_long <- melt(sweet_corn_pest, id.vars = c("Site", "USDA.hardiness.Zone", "Lat", "Long", "Year", "Week", "Date", "Day"), measure.vars = c("ECB.E", "ECB.Z", "CEW", "FAW", "WBC"), variable.name = 'Pest', value.name = 'Count')

head(sweet_corn_pest_long)

##     Site USDA.hardiness.Zone            Lat           Long Year Week       Date
## 1 Accord                  6a  41°46'56.41"N  74°14'44.28"W 2021   21 2021-05-25
## 2 Accord                  6a  41°46'56.41"N  74°14'44.28"W 2021   22 2021-06-01
## 3 Accord                  6a  41°46'56.41"N  74°14'44.28"W 2021   23 2021-06-08
## 4 Accord                  6a  41°46'56.41"N  74°14'44.28"W 2021   24 2021-06-15
## 5 Accord                  6a  41°46'56.41"N  74°14'44.28"W 2021   25 2021-06-22
## 6 Accord                  6a  41°46'56.41"N  74°14'44.28"W 2021   26 2021-06-29
##   Day  Pest Count
## 1 145 ECB.E    NA
## 2 152 ECB.E    NA
## 3 159 ECB.E    NA
## 4 166 ECB.E    NA
## 5 173 ECB.E     0
## 6 180 ECB.E     0

Site Data

Upload csv with latitude and longitude for all sites. There are 89 unique values for sites.

sites <- read.delim("NYSIPM_Sweet_Corn_1993_2022_Sites.csv", sep = ",")

sites <- sites %>% 
  mutate(Site = as.factor(Site)) %>%
  select(-c('X', 'X.1', 'X.2', 'X.3', 'X.4', 'X.5', 'X.6', 'X.7'))

length(levels(sites$Site))

## [1] 89

head(sites)

##           Site USDA.hardiness.Zone            Lat Lat.Decimal           Long
## 1       Accord                  6a  41°46'56.41"N    41.78234  74°14'44.28"W
## 2      Accord                   6a  41°46'56.41"N    41.78234  74°14'44.28"W
## 3 Adams Center                  4b  43°51'52.56"N    43.86460  75°57'53.64"W
## 4        Afton                  5b  42°13'57.32"N    42.23259   75°31'3.54"W
## 5       Albion                  6a                         NA               
## 6    Amsterdam                  5a  43° 1'18.48"N    43.02180  74°10'48.72"W
##   Long.Decimal
## 1    -74.24563
## 2    -74.24563
## 3    -75.96490
## 4    -75.51765
## 5           NA
## 6    -74.18020

Plot Sites

Explore the north-south distribution of the monitoring sites.

sites_with_coord <- sites %>% drop_na()

mapview(sites_with_coord, xcol = "Long.Decimal", ycol = "Lat.Decimal", crs = 4269, grid = FALSE)

Exploratory Data Analysis

Create initial visualizations of the pest count data. Exploring differences in years, species, and sites.

Pest Counts 1993-2022, Faceted by Species

Each species shows different patterns in abundance over time. ECB has decreased over time, while CEW, FAW, and WBC have increased.

sweet_corn_pest_long %>%
  ggplot(aes(Date, Count)) + 
  geom_line() +
  ggtitle('Pest Counts 1993-2022') +
  facet_wrap(~ Pest)

## Warning: Removed 2 rows containing missing values (`geom_line()`).

ECB_E Count, Faceted by Year

Earlier years are showing higher counts than more recent years. Most years are showing a bimodal pattern.

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'ECB.E') %>%
  ggplot(aes(Day, Count)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('ECB_E Counts 1993-2022') +
  facet_wrap(~ Year)

## Warning: Removed 261 rows containing missing values (`geom_line()`).

## Warning: Removed 2872 rows containing missing values (`geom_point()`).

ECB_Z Count, Faceted by Year

Earlier years are showing higher counts than more recent years. Some years are showing a bimodal pattern, others are trimodal.

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'ECB.Z') %>%
  ggplot(aes(Day, Count)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('ECB_Z Counts 1993-2022') +
  facet_wrap(~ Year)

## Warning: Removed 255 rows containing missing values (`geom_line()`).

## Warning: Removed 2881 rows containing missing values (`geom_point()`).

CEW Count, Faceted by Year

CEW count seems to peak later in the season. Mostly unimodal.

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'CEW') %>%
  ggplot(aes(Day, Count)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('CEW Counts 1993-2022') +
  facet_wrap(~ Year)

## Warning: Removed 425 rows containing missing values (`geom_line()`).

## Warning: Removed 3488 rows containing missing values (`geom_point()`).

FAW Count, Faceted by Year

FAW count seems to peak later in the season. Mostly unimodal.

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'FAW') %>%
  ggplot(aes(Day, Count)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('FAW Counts 1993-2022') +
  facet_wrap(~ Year)

## Warning: Removed 524 rows containing missing values (`geom_line()`).

## Warning: Removed 4093 rows containing missing values (`geom_point()`).

WBC Count, Faceted by Year

WBC count seems to peak earlier in the season than CEW and FAW. Mostly unimodal.

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'WBC') %>%
  ggplot(aes(Day, Count)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('WBC Counts 1993-2022') +
  facet_wrap(~ Year)

## Warning: Removed 5797 rows containing missing values (`geom_line()`).

## Warning: Removed 7908 rows containing missing values (`geom_point()`).

All Pest Counts, Faceted by Year

Overlaid pest counts for each year to see differences in emergence and peaks.

sweet_corn_pest_long %>%
  ggplot(aes(Day, Count, col = Pest)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('Sweet Corn Pest Counts 1993-2022') +
  facet_wrap(~ Year)

## Warning: Removed 7262 rows containing missing values (`geom_line()`).

## Warning: Removed 21242 rows containing missing values (`geom_point()`).

Sweet Corn Pest Count, 1993-1995

ECB-E, ECB-Z,and FAW are the most prevalent pests during this period of time.

sweet_corn_pest_long %>%
  dplyr::filter(((Year >= 1993) & (Year <= 1995))) %>%
  ggplot(aes(Date, Count, col = Pest)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('Sweet Corn Pest Counts 1993-1995')

## Warning: Removed 875 rows containing missing values (`geom_line()`).

## Warning: Removed 923 rows containing missing values (`geom_point()`).

Sweet Corn Pest Count, 2020-2022

CEW, FAW, and WBC are the most prevalent. WBC monitoring did not start until 2010.

sweet_corn_pest_long %>%
  dplyr::filter(((Year >= 2020) & (Year <= 2022))) %>%
  ggplot(aes(Date, Count, col = Pest)) + 
  geom_line() +
  geom_point(size = 0.25) +
  ggtitle('Sweet Corn Pest Counts 2020-2022')

## Warning: Removed 63 rows containing missing values (`geom_line()`).

## Warning: Removed 2997 rows containing missing values (`geom_point()`).

ECB_E Count, Faceted by Site

Sites with larger (or more consistent throughout the season) observations of ECB_E are -

• Baldwinsville
• Batavia
• Bellona
• Byron
• Eden
• Farmington*
• Hall
• Hamlin
• King Ferry
• Kirkville
• LeRoy
• Lockport
• Medina
• Owego
• Penn Yan
• Spencerport
• Williamson

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'ECB.E') %>%
  ggplot(aes(Date, Count)) + 
  geom_line(size = 1) +
  geom_point(size = 0.5) +
  ggtitle('ECB_E Counts 1993-2022 by Site') +
  facet_wrap(~ Site)

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 2872 rows containing missing values (`geom_point()`).

ECB_Z Count, Faceted by Site

Sites with larger (or more consistent) observations of ECB_Z are -

• Baldwinsville
• Batavia
• Bellona
• Eden
• Farmington
• Hall
• Hamlin
• Kennedy
• King Ferry
• Kirkville
• LeRoy
• Lockport
• Medina
• Owego
• Penn Yan
• Rush
• Spencerport
• Williamson

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'ECB.Z') %>%
  ggplot(aes(Date, Count)) + 
  geom_line(size = 1) +
  geom_point(size = 0.5) +
  ggtitle('ECB_Z Counts 1993-2022 by Site') +
  facet_wrap(~ Site)

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 2881 rows containing missing values (`geom_point()`).

CEW Count, Faceted by Site

Sites with larger (or more consistent) observations of CEW are -

• Avon
• Baldwinsville
• Batavia
• Bellona
• Eden
• Farmington
• Feura Bush
• Florida
• Geneva
• Geneva 1
• Hall
• Hamlin
• Kennedy
• Kinderhook
• King Ferry
• Kirkville
• LeRoy
• Lockport
• Medina
• Oswego
• Owego
• Penn Yan
• Ransomville
• Sherwood
• Spencerport
• Williamson

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'CEW') %>%
  ggplot(aes(Date, Count)) + 
  geom_line(size = 1) +
  geom_point(size = 0.5) +
  ggtitle('CEW Counts 1993-2022 by Site') +
  facet_wrap(~ Site)

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 3488 rows containing missing values (`geom_point()`).

FAW Count, Faceted by Site

Sites with larger (or more consistent) observations of FAW are -

• Baldwinsville
• Batavia
  
• Bellona
• Eden
• Farmington
• Hall
• Hamlin
• Kennedy
• Kinderhook
• King Ferry
• Kirkville
• LeRoy
• Lockport
• Owego
• Pavillion
• Penn Yan
• Peru
• Sherwood
• Spencerport
• Williamson

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'FAW') %>%
  ggplot(aes(Date, Count)) + 
  geom_line(size = 1) +
  geom_point(size = 0.5) +
  ggtitle('FAW Counts 1993-2022 by Site') +
  facet_wrap(~ Site)

## Warning: Removed 9 rows containing missing values (`geom_line()`).

## Warning: Removed 4093 rows containing missing values (`geom_point()`).

WBC Count, Faceted by Site

Sites with larger (or more consistent) observations of WBC are -

• Baldwinsville
• Batavia
• Bellona
• Eden
• Oswego
• Pavillion
• Penn Yan
• Peru
• Plattsburg
• Plessis
• Preble
• Williamson

sweet_corn_pest_long %>%
  dplyr::filter(Pest == 'WBC') %>%
  ggplot(aes(Date, Count)) + 
  geom_line(size = 1) +
  geom_point(size = 0.5) +
  ggtitle('WBC Counts 1993-2022 by Site') +
  facet_wrap(~ Site)

## Warning: Removed 9 rows containing missing values (`geom_line()`).

## Warning: Removed 7908 rows containing missing values (`geom_point()`).

ECB_E Count per Year, 1993-2022

Shows a decrease over time in total observations per year.

sweet_corn_pest_long %>%
  replace(is.na(sweet_corn_pest_long), 0) %>%
  dplyr::filter(Pest == 'ECB.E') %>%
  group_by(Year) %>%
  summarise(Total = sum(Count)) %>%
  ggplot(aes(x = Year, y = Total)) + 
  geom_bar(stat = 'identity') +
  ggtitle('ECB.E Counts 1993-2022')

ECB_Z Count per Year, 1993-2022

Shows a decrease over time in total observations per year.

sweet_corn_pest_long %>%
  replace(is.na(sweet_corn_pest_long), 0) %>%
  dplyr::filter(Pest == 'ECB.Z') %>%
  group_by(Year) %>%
  summarise(Total = sum(Count)) %>%
  ggplot(aes(x = Year, y = Total)) + 
  geom_bar(stat = 'identity') +
  ggtitle('ECB.Z Counts 1993-2022')

CEW Count per Year, 1993-2022

Shows an increase over time in total observations per year.

sweet_corn_pest_long %>%
  replace(is.na(sweet_corn_pest_long), 0) %>%
  dplyr::filter(Pest == 'CEW') %>%
  group_by(Year) %>%
  summarise(Total = sum(Count)) %>%
  ggplot(aes(x = Year, y = Total)) + 
  geom_bar(stat = 'identity') +
  ggtitle('CEW Counts 1993-2022')

FAW Count per Year, 1993-2022

Shows an increase over time in total observations per year.

sweet_corn_pest_long %>%
  replace(is.na(sweet_corn_pest_long), 0) %>%
  dplyr::filter(Pest == 'FAW') %>%
  group_by(Year) %>%
  summarise(Total = sum(Count)) %>%
  ggplot(aes(x = Year, y = Total)) + 
  geom_bar(stat = 'identity') +
  ggtitle('FAW Counts 1993-2022')

WBC Count per Year, 1993-2022

Shows an increase over time in total observations per year.

sweet_corn_pest_long %>%
  replace(is.na(sweet_corn_pest_long), 0) %>%
  dplyr::filter(Pest == 'WBC') %>%
  group_by(Year) %>%
  summarise(Total = sum(Count)) %>%
  ggplot(aes(x = Year, y = Total)) + 
  geom_bar(stat = 'identity') +
  ggtitle('WBC Counts 1993-2022')